Personalized Learning Path Generation Based On Neuro-cognitive Collaborative Filtering

With the rapid development of information technology in education, teaching resources have increased dramatically and the teaching environment has improved. However, resource overload and learning disorientation are challenges faced by learners. This study proposes a personalized learning path generation model based on neurocognitive collaborative filtering technology. By analyzing learners' personalized static parameters and dynamic learning behavior data, explicit and implicit weak knowledge points are identified, and optimized learning paths are generated. The empirical results show that the method significantly improves learners' learning outcomes and satisfaction over traditional methods. This study promotes the development of personalized learning design and effective learning support systems with important implications for educational practice.